Analyzing the influencing factors of unplanned readmission within one year after cadaveric renal transplantation

doi:10.21203/rs.3.rs-4580261/v1

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Analyzing the influencing factors of unplanned readmission within one year after cadaveric renal transplantation

https://doi.org/10.21203/rs.3.rs-4580261/v1

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To investigate the factors influencing unplanned readmission within one year following cadaveric renal transplantation. Clinical data of kidney transplant recipients from the organ transplantation department of a tertiary general hospital in Guizhou Province, collected between April 2017 and December 2022, were retrospectively analyzed. Recipients were categorized into an unplanned readmission group and a non-readmission group based on whether unplanned readmission occurred within one year post-surgery. Univariate analysis and conditional backward stepwise regression was employed to identify variables, and binary logistic regression analysis was conducted to determine the independent influencing factors of unplanned readmission within one year after cadaveric renal transplantation. Unplanned readmission was observed in 183 out of 479 recipients (38.20%). Univariate analysis and conditional backward stepwise regression indicated that fall risk, albumin, total protein, serum creatinine, Cystatin C, medical insurance, length of stay, rejection reaction, blood transfusion during hospitalization, occupations, smoking history, drinking history and comorbidities were associated with unplanned readmission within one year after cadaveric renal transplantation. Binary logistic regression analysis revealed that rejection reaction(OR=2.323, 95%CI: 1.009-5.345), comorbidities greater than or equal to three (OR=1.887, 95%CI: 1.095-3.111), fall risk(OR=0.387, 95%CI: 0.162-0.927) , and on-the-job(OR=2.949, 95%CI: 1.789-4.862) were independent factors influencing unplanned readmission within one year after cadaveric renal transplantation.Consequently, upon discharge, medical staff should thoroughly assess the risk of unplanned readmission and develop individualized intervention plans based on these risk factors to reduce the incidence of unplanned readmissions, alleviate disease burden, and optimize the allocation of medical resources.

Health sciences/Risk factors

Health sciences/Nephrology

Health sciences/Nephrology/Kidney

Health sciences/Nephrology/Renal replacement therapy

Cadaveric renal transplantation

Recipients

Unplanned readmissions

Influencing factors

Unplanned readmission refers to the readmission of hospitalized patients to medical institutions for the same or related conditions within a certain period after discharge, resulting in a decline in patients' quality of life, increased medical risks, and heightened economic burden. Studies have shown that unplanned readmission in kidney transplant recipients leads to a 41% increase in the risk of death of the recipient^[1], a decrease in graft function^[2], and a 3.02 times higher risk of late repeat readmission^[3] than other patients. Unplanned readmission not only increases the risk of death and the decline or loss of graft function, but also increases the disease burden of recipients, their families and society. In Canada, the average medical cost of unplanned readmission after kidney transplant recipients is 11,606 CAD^[4], in Poland 12,718 PLN^[5]. In China, unplanned readmission caused by community-acquired infections also leads to an increase of 33,150.8 yuan in hospitalization costs of recipients^[6]. The rate of unplanned readmissions has been rising, highlighting the issue of inefficient allocation of medical resources. Governments worldwide have introduced policies to address this problem. In 2007, the Centers for Medicare & Medicaid Services in the United States pioneered the use of unplanned readmission rates as an indicator to monitor the quality of hospital care^[7]. In 2011, the former Ministry of Health of China issued the Medical Quality Management and Control Indicators for Tertiary General Hospitals (2011 edition), designating the unplanned readmission rate as a crucial metric for assessing the medical and nursing quality of tertiary general hospitals^[8]. In 2023, the Action Plan for Comprehensive Medical Quality Improvement (2023–2025)^[9] and the Action Plan for Further Improving Nursing Services (2023–2025)^[10] issued by the National Health Commission, underscored the importance of enhancing extended medical services, improving follow-up care quality for patients undergoing complex surgeries, and offering one-stop diagnosis and treatment services. These measures aim to reduce the unplanned readmission rate of discharged patients.

As a phenomenon that consumes significant medical resources, unplanned readmission is particularly prevalent among patients with complex conditions^[11]. Kidney transplantation, as the optimal treatment for patients with end-stage chronic kidney disease, involves greater clinical complexity than general surgery. Maintaining the recipient's immune function in relative balance post-surgery is a significant challenge. Insufficient immune function can lead to infection, while excessive immune function may cause rejection, potentially resulting in readmission. Studies have indicated that the unplanned readmission rate for Chinese kidney transplant recipients within one year post-surgery ranges from 20–22.59%^[12–13]. Unplanned readmission for kidney transplant recipients is highly detrimental^[14]. However, previous studies on unplanned readmissions in China have predominantly focused on chronic diseases, such as chronic obstructive pulmonary disease^[15] and AIDS^[16], with limited attention given to unplanned readmissions among organ transplant recipients^[17].

After reviewing the literature, it was found that most scholars, both domestically and internationally, have chosen "within 1 year" as the time frame for conducting research on unplanned readmissions^[18–26]. Building on this, our study retrospectively analyzed the case data of kidney transplant recipients who underwent cadaveric renal transplantation in the organ transplantation department of a tertiary general hospital in Guizhou Province from April 2017 to December 2022. By employing univariate analysis, conditional backward stepwise regression and binary logistic regression analysis, we aimed to identify the influencing factors of unplanned readmission within one year after cadaveric renal transplantation. This analysis seeks to enhance medical staff's awareness of risk factors, implement preventive measures to reduce the rate of unplanned readmissions, improve patient outcomes, and alleviate the disease burden.

Research objects

This was a retrospective study. Recipients who underwent cadaveric renal transplantation in the organ transplantation department of a tertiary general hospital in Guizhou Province from April 2017 to December 2022 were selected using convenience sampling.

Inclusion criteria:

(1) The initial admission diagnosis met the 2012 KDIGO Clinical Practice Guidelines for the evaluation and management of Chronic Kidney Disease staging criteria for renal failure ^[27];

(2) Age 18–70 years old;

(3) First renal transplantation;

(4) Received a cadaveric renal transplant.

Exclusion criteria:

(1) Multiple organ transplantations;

(2) Transfer or death during the previous hospitalization;

(3) Postoperative follow-up of less than one year;

Data collection

Clinical data were extracted by researchers from the hospital information system, encompassing recipient demographics, transplantation specifics, laboratory results, and donor information. For those with unplanned readmission, the timing and reasons were documented. Recipient demographics: age, gender, nationality, marital status, education level, occupation, residence, type of medical insurance, body mass index (BMI), primary kidney disease, complications, alcohol and smoking history, fall risk, self-care ability, and post-transplant hospital stay.Transplantation specifics: dialysis modality, dialysis vintage, immunoinduction regimen, panel reactive antibody (PRA), donor-specific antibody (DSA), delayed graft function (DGF), rejection episodes, and types of regular immunosuppressants. Laboratory test results at discharge: blood routine, liver function, renal function, serum electrolytes, and tacrolimus drug levels. Donor information: age, gender, and type of kidney source.

Statistical methods

SPSS 26.0 was employed for statistical analysis. Recipients were divided into donor after cardiac death group, donation after brain death group and donation after brain plus cardiac death group according to the type of kidney source, and baseline data were compared. Non-normally distributed data were described using the median and quartiles [M(P₂₅,P₇₅)], and compared with Kruskal-Wallis test (H tests). Kidney transplant recipients were categorized into unplanned readmission and non-readmission groups based on whether they experienced unplanned readmission for the same or related conditions within one year post-operation. Normally distributed and homoscedastic measurement data were described as mean ± standard deviation (\(\stackrel{-}{x}\)±s), and compared using t-tests. Non-normally distributed data were described using the median and quartiles [M(P₂₅,P₇₅)], and compared with Mann-Whitney U tests. Categorical data were reported as counts and component ratio [n(%)], compared using χ² or Fisher's exact tests. Rank data comparisons utilized rank sum tests. Variables were screened via conditional backward stepwise regression^[28], followed by binary logistic regression to identify independent factors influencing unplanned readmission within one year post-transplantation. A P-value < 0.05 was deemed statistically significant.

Ethical considerations

It was a retrospective study, and informed consent of patients was waived by the Ethics Committee of the Affiliated Hospital of Guizhou Medical University.

Ethical Approval

This research was carried out following the institutional and national ethical guidelines for human studies and according to the ethical principles outlined in the Declaration of Helsinki. All the procedures in the present study were approved by the Medical Ethics Committee of the Affiliated Hospital of Guizhou Medical University (Approval number :152,2024).

Characteristics of the subjects

From April 2017 to December 2022, 731 patients underwent kidney transplantation. According to inclusion and exclusion criteria, 252 recipients were excluded from this study. Exclusions included 125 recipients with less than one year of follow-up, 20 recipients under 18 years old, 5 recipients who died, 18 recipients who underwent a second kidney transplantation, 83 received living donor kidney transplantation and 1 recipient who underwent multiple organ transplantation. A total of 479 renal transplant recipients were ultimately included, as shown in Figure 1.

The study included 479 recipients with an average age of 40.33 years, 321 males (67.01%), 79.54% married, 13.15% from ethnic minorities, 49.27% and 20.25% with primary renal disease due to hypertensive nephropathy and glomerulonephritis. (details in Table 1).

Incidence of unplanned readmission within 1 year after renal transplantation

Among the 479 renal transplant recipients, 183 experienced unplanned readmission within one year post-operation, an incidence of 38.20%. The median time to unplanned readmission was 4 months post-transplantation, with an average re-hospital stay of 18.87 days. The primary reason for unplanned readmission was infection, with pulmonary infection accounting for 35.52% and urinary tract infection accounting for 12.57% (details in Table 2).

Influencing factors of unplanned readmission within 1 year after renal transplantation

Univariate analysis revealed that serum creatinine, cystatin C, total protein, albumin, occupation, comorbidities, rejection reaction, blood transfusion during hospitalization, fall risk, length of hospital stay, and drinking history were statistically different between the unplanned readmission group and the non-readmission group (Table 3).

In conditional backward stepwise regression (alpha in = 0.05, alpha out = 0.1), serum creatinine, albumin, number of comorbidities, length of hospital stay post-transplantation, fall risk, occupation, medical insuranceand and smoking history were associated with unplanned readmission within one year post-kidney transplantation (Table 4).

Significant variables in univariate analysis and conditional backward stepwise regression were included in the regression equation (Table 5). Binary logistic regression analysis revealed that rejection reaction(OR=2.323, 95%CI: 1.009-5.345), comorbidities greater than or equal to three (OR=1.887, 95%CI: 1.095-3.111), fall risk (OR=0.387, 95%CI: 0.162-0.927) , and on-the-job (OR=2.949, 95%CI: 1.789-4.862) were independent factors influencing unplanned readmission within one year after cadaveric renal transplantation(Table 6).

The one-year unplanned readmission rate for kidney transplant recipients in this study was 38.20%, surpassing the 20.00-22.59% reported by other transplant centers in China^[12-13]. However, this rate is lower than that reported by some international organ transplant centers, such as Canada (41.30%)^[26], the United States (53.00%)^[29]. The variations in the incidence of unplanned readmission within one year post-kidney transplantation can be attributed to several factors, including the types of renal transplantation, the demographic characteristics of the recipients, the operational volume of the transplant center, the department's approach to diagnosis and treatment, and the nature of the research conducted.

Most recipients at our organ transplant center are local residents of Guizhou Province. According to a 2020 survey on health literacy levels in Guizhou Province^[30], the health literacy rate is only 18.83%, which is lower than that of residents in eastern regions. Low health literacy may result in poor adherence to health management protocols. The risk of postoperative medication non-adherence in recipients with poor preoperative adherence was found to be 37.65 times higher than in those with good preoperative adherence^[31]. Poor medication adherence leads to unsatisfactory clinical outcomes, such as graft failure^[32]. This suggests that medical staff should assess the health literacy of recipients upon discharge and provide individualized health education as necessary.

Our hospital is the first in Guizhou Province to obtain kidney transplantation qualifications, offering "one-stop" diagnostic and treatment services. All kidney transplant recipients receive their pre-transplant preparation, perioperative management, post-discharge review, follow-up, and other health management within our department. This comprehensive approach enables us to thoroughly understand each patient's medical history and reinforces a "patient-centered" and "disease-as-a-chain" concept for individualized diagnosis and treatment. When the condition of kidney transplant recipients changes, the integrated diagnostic and treatment services provided by our department meet their medical needs, making them more inclined to seek treatment here again, reflecting the department's brand effect. This may partly explain why the unplanned readmission rate in our study is higher compared to other transplant centers in the country.

The lower rate of unplanned readmission observed in this study compared to some foreign organ transplant centers may be attributed to the use of national large databases by foreign scholars to investigate the readmission rates of kidney transplant recipients, whereas this study focused solely on recipients at our hospital's organ transplant center. Future research should conduct multi-center surveys of unplanned readmissions within one year post-kidney transplantation in Guizhou Province or across China to enhance the representativeness of the findings. Additionally, differences in the distribution of donated organs domestically and internationally, as well as the substantial variation in the volume of operations performed at different organ transplant centers, may contribute to the observed disparities^[33].

Our study revealed that the reasons for unplanned readmission within one year following cadaveric renal transplantation are multifaceted, with infection (54.10%) being the predominant cause. This finding aligns with conclusions drawn by domestic scholars^[12-13]. Studies indicate that infection is also a leading cause of readmission following other solid organ transplants, including lung and liver transplants^[34-36]. It is recommended that future efforts focus on developing risk prediction models for post-kidney transplantation infections. These models could aid medical staff in enhancing early management of recipients at high risk for infection, thereby reducing unnecessary readmissions and alleviating the disease burden. Additionally, rejection reaction, adverse drug reactions, and so on are significant contributors to unplanned readmission post-renal transplantation. Although their incidence is lower than that of infections, they still warrant close attention from medical personnel and diligent self-monitoring by recipients and their families.

The occurrence of rejection during the initial hospitalization for transplantation was correlated with unplanned readmission within one year post-surgery, consistent with the findings of Tavares MG et al^[37]. Numerous HLA mismatches, inadequate immunosuppressive drug dosages, and poor recipient compliance can contribute to rejection^[38]. Studies have indicated that achieving zero HLA mismatches^[39] and reducing the number of HLA mismatches^[40] can significantly decrease the incidence of unplanned readmissions. It is recommended to strive for zero HLA mismatches before transplantation to mitigate the risk of unplanned readmission. However, immune-induction therapy with antithymocyte globulin in patients results in neutropenia and increases their risk of infection, which leads to recipient readmission ^[41-42]. Therefore, personalized before transplantation pretreatment solutions and standardized adjust drug row is very important to reduce readmission after renal transplantation recipients. Recipients with poor compliance often fail to adhere to medication schedules, leading to insufficient immunosuppressive drug concentrations^[43] and a 2.6-fold increase in the risk of acute rejection^[44] within 24 months post-transplantation. Implementing individualized health education can enhance treatment compliance, improve recipients' self-management abilities, thereby reducing the occurrence of unplanned readmissions.

The risk of unplanned readmission in recipients with more than 3 comorbidities was 1.323 times higher than that in those with less than 3 comorbidities. The more comorbidities the patients have before surgery, the worse their physical function will be. In addition, the stress caused by surgical trauma will make the patients' physical condition at a lower level^[45]. Wei found that the more underlying diseases, the higher the readmission rate and the greater the risk of death^[46]. It is suggested that individualized disease management should be carried out for recipients with multimorbidity to reduce the unplanned readmission rate and the risk of death. Dai Shoudaet al. explored the effect of the severity of basic diseases on the prognosis of surgical treatment of femoral intertrochanteric fractures in elderly patients, and found that the more serious the preoperative basic diseases, the worse the surgical prognosis^[47]. It is suggested that we should not only pay attention to the number of postoperative underlying diseases, but also assess the severity of the disease, and adopt a multidisciplinary cooperative medical model to guide the synchronous treatment of recipients in order to promote their postoperative recovery.

Fall risk and On-the-job emerges as another independent risk factor for unplanned readmission within one year post-renal transplantation. Renal transplant recipients at risk of falling are less prone to unplanned readmission than those without risk of falling, which may indicate that those at risk of falling are more concerned about their own health. It may be that their self-care and management skills are better than those of non-fall recipients. Such readmissions often exacerbate the economic burden of disease. Employed kidney transplant recipients generally have a stable income and receive greater social support compared to their unemployed counterparts^[48]. When unexpected complications arise post-transplant, they possess the financial means and preference for hospitalization to seek professional treatment.

This study has the following limitations. First, this study was a single-center retrospective analysis, and the results may not be representative of other transplantation centers. In addition, the influencing factors included in this study may be limited. Future multi-center, large-sample prospective studies are recommended to enhance the generalizability and credibility of the results.

Unplanned readmission, a phenomenon that consumes substantial medical resources, has garnered significant attention from healthcare systems. This study identified infection as the primary cause of unplanned readmission within one year post-renal transplant. Furthermore, rejection reaction, comorbidities greater than or equal to three, fall risk, and on-the-job were independent factors influencing unplanned readmission within one year after cadaveric renal transplantation. Therefore, upon discharge, medical staff should thoroughly assess the risk of unplanned readmission and develop individualized intervention plans based on these risk factors to mitigate the incidence of unplanned readmissions, alleviate the disease burden, and ensure the optimal allocation of medical resources.

Acknowledgements

Thanks for the guidance and strong support of the supervisor and colleagues in the department during this research and paper collaboration.

Author contributions

Weiwei Cao: research design, data collection and thesis writing; Minghuan Zhong: Project conception; Zhou Sun, Guofu Liang, Fu Yan, Chao Liu: data collation, data analysis, table production;Li Ma, Kejing Zhu: research guidance, paper revision; Bei Ding, Yulin Niu: research guidance, paper review, and funding support.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Funding

Science and Technology Project of Guizhou Province (ZK[2023]380); Guizhou Medical University Nursing Special Project YJ21053

Competing interests

The authors declare no competing interests.

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Table 1. Characteristics of the Study Population

Variables of interest	Donor after cardiac death	Donation after brain death	Donation after brain plus cardiac death	Value of statistics	P-value
Age[years,M(P₂₅,P₇₅)]	40(33.75,47.25)	40(32,49)	36(29,44)	H=4.614	0.100
Sex[n(%)] Male Female	54(77.10%) 16(22.90%)	239(65.70%) 125(34.30%)	28(62.2%) 17(37.80%)	=4.018	0.134
Body mass index[Kg/m2,M(P25,P75)]	22.55(19.88,25.43)	21.95(19.90,24.58)	21.20(19.00,23.60)	H=5.342	0.069
Ethnic groups[n(%)] Han nationality Ethnic minorities	57(81.40%) 13(18.60%)	320(87.90%) 44(12.10%)	39(86.70%) 6(13.30%)	=2.162	0.339
Place of residence[n(%)] Rural/town City	27(38.60%) 43(61.40%)	165(45.30%) 199(54.70%)	13(28.90%) 32(71.10%)	=5.020	0.081
Marital status[n(%)] Unmarried Married Divorced/widowed	14(20.00%) 53(75.70%) 3(4.30%)	55(15.10%) 296(81.30%) 13(3.60%)	10(22.20%) 32(71.10%) 3(6.70%)	F=4.175	0.365
Educational level[n(%)] Primary and below Primary to junior high school Middle to high school Bachelor degree or above	4(5.70%) 14(20.00%) 10(14.30%) 42(60.00%)	27(7.40%) 82(22.50%) 60(16.50%) 195(53.60%)	0(0.00%) 12(26.70%) 8(17.80%) 25(55.60%)	H=1.171	0.557
Occupations[n(%)] Yes No	43(61.40%) 27(38.60%)	223(61.30%) 141(38.70%)	15(33.30%) 30(66.70%)	=0.500	0.779
Variables of interest	Donor after cardiac death	Donation after brain death	Donation after brain plus cardiac death	Value of statistics	P-value
Medical Insurance[n(%)] Medical insurance for urban employees Medical insurance for urban residents New rural cooperative medical insurance Out of pocket Other	39(55.70%) 10(14.30%) 15(21.40%) 0(0.00%) 6(8.60%)	185(50.82%) 53(14.56%) 74(20.33%) 14(3.85%) 38(10.44%)	24(53.30%) 2(4.40%) 12(26.70%) 4(8.90%) 3(6.70%)	F=10.528	0.208
Primary nephropathy[n(%)] Glomerulonephritis Renal hypertension Polycystic kidney disease IgA nephropathy Renal diabetes, Others	20(28.60%) 34(48.60%) 0(0.00%) 2(2.90%) 4(5.70%) 10(14.30%)	69(19.00%) 173(47.50%) 12(3.30%) 22(6.00%) 22(6.00%) 66(18.10%)	8(17.80%) 29(64.40%) 1(2.20%) 0(0.00%) 0(0.00%) 7(15.60%)	=14.537	0.150
Smoking history[n(%)] Yes No	14(20.00%) 56(80.00%)	70(19.20%) 294(80.80%)	4(8.90%) 41(91.10%)	=3.001	0.223
Drinking history[n(%)] Yes No	10(14.30%) 60(85.70%)	50(13.70%) 314(86.30%)	4(8.90%) 41(91.10%)	=0..873	0.646
Dialysis mode[n(%)] Hemodialysis Peritoneal dialysis Mixed dialysis None	61(87.14%) 7(10.00%) 1(1.43%) 1(1.43%)	287(78.85%) 54(14.84%) 12(3.30%) 11(3.00%)	38(84.44%) 6(13.33%) 1(2.22%) 0(0.00%)	F=2.697	0.842
Variables of interest	Donor after cardiac death	Donation after brain death	Donation after brain plus cardiac death	Value of statistics	P-value
Dialysis vintage[n(%)] < 1 year 1-3 years ＞3 years	21(30.00%) 31(44.30%) 18(25.70%)	100(27.50%) 181(49.70%) 83(22.80%)	13(28.90%) 26(57.80%) 6(13.30%)	=3.179	0.528
Comorbidities[n(%)] ＜3 ≥3	62(88.60%) 8(11.40%)	294(80.80%) 70(19.20%)	41(91.10%) 4(8.90%)	=4.890	0.087
Immunoinduction regimen[n(%)] ATG R-ATG ATG+Balliximab ATG+Rituximab ATG+R-ATG R-ATG+Rituximab R-ATG+Balliximab Cyclophosphamide	19(27.14%) 48(68.57%) 0(0.00%) 2(2.86%) 0(0.00%) 0(0.00%) 0(0.00%) 1(1.43%)	105(28.85%) 199(54.67%) 7(1.92%) 41(11.26%) 3(0.82%) 2(0.55%) 7(1.92%) 0(0.00%)	5(11.11%) 35(77.78%) 4(8.89%) 1(2.22%) 0(0.00%) 0(0.00%) 0(0.00%) 0(0.00%)	F=30.962	0.002
Donor specific antibody[n(%)] Negative Positive	70(100.00%) 0(0.00%)	359(98.60%) 5(1.40%)	45(100.00%) 0(0.00%)	F=0.448	1.000
Panel reactive antibody[n(%)] Negative Positive	69(98.57%) 1(1.43%)	339(93.10%) 25(6.90%)	44(97.78%) 1(2.22%)	F=3.801	0.124
Rejection reaction[n(%)] Yes No	4(5.70%) 66(94.30%)	26(7.10%) 338(92.90%)	2(4.40%) 43(95.60%)	F=0.296	0.857
Variables of interest	Donor after cardiac death	Donation after brain death	Donation after brain plus cardiac death	Value of statistics	P-value
Delayed graft function[n(%)] Yes No	11(15.70%) 59(84.30%)	51(14.00%) 313(86.00%)	3(6.70%) 42(93.30%)	=2.163	0.339
Blood Transfusion during Hospitalization[n(%)] Yes No	6(8.60%) 64(91.40%)	33(9.10%) 331(90.90%)	1(2.22%) 44(97.78%)	=2.456	0.293
Immunosuppressive drug regimen[n(%)]* regimen-A regimen-B regimen-C regimen-D regimen-E regimen-F regimen-G	57(81.43%) 8(11.43%) 1(1.43%) 2(2.86%) 2(2.86%) 0(0.00%) 0(0.00%)	331(90.93%) 25(6.87%) 2(0.55%) 3(0.82%) 1(0.27%) 1(0.27%) 1(0.27%)	41(91.11%) 2(4.44%) 0(0.00%) 2(4.44%) 0(0.00%) 0(0.00%) 0(0.00%)	F=16.770	0.110
Fall risk [n(%)] Yes NO	61(87.10%) 9(12.90%)	350(96.20%) 14(3.80%)	41(91.10%) 4(8.90%)	F=9.224	0.007
Self-care ability[n(%)] No dependence Need for help	50(71.40%) 20(28.60%)	225(61.80%) 139(38.20%)	34(75.60%) 11(24.40%)	=5.018	0.081
Serum creatinine[umol/L,M(P25,P75)]	140.50(108.97,184.00)	130.00(100.00,183.85)	124.00(96.97,154.51)	H=3.364	0.186
Cystatin C[mg/L,M(P25,P75)]	2.10(1.75,2.81)	2.18(1.70,2.98)	1.95(1.57,2.53)	H=3.362	0.186
Variables of interest	Donor after cardiac death	Donation after brain death	Donation after brain plus cardiac death	Value of statistics	P-value
White cell count[G/L,M(P25,P75)]	7.28(5.68,8.68)	6.32(5.00,8.37)	6.95(5.08,9.47)	H=6.218	0.045
Total protein[G/L,M(P25,P75)]	67.05(63.43,72.38)	65.55(61.50,70.18)	66.03(62.86,70.15)	H=5.241	0.073
Albumin[G/L,M(P25,P75)]	44.90(42.55,48.93)	43.30(40.43,46.10)	44.88(42.15,48.96)	H=16.323	＜0.001
Globulin[G/L,M(P25,P75)]	22.15(19.80,24.50)	22.55(19.93,25.18)	21.00(18.99,23.30)	H=6.102	0.047
Hemoglobin[G/L,M(P25,P75)]	102.50(88.75,116.25)	96.00(84.00,109.75)	106.00(91.00,118.00)	H=10.558	0.005
Na⁺[mmol/L,M(P25,P75)]	140.53(138.36,141.90)	140.59(139.07,142.30)	140.40(138.28,141.59)	H=2.478	0.290
P⁺[mmol/L,M(P25,P75)]	0.83(0.64,1.14)	0.76(0.60,0.95)	0.78(0.60,0.98)	H=3.598	0.165
k⁺[mmol/L,M(P25,P75)]	4.42(4.07,4.87)	4.30(3.94,4.64)	4.26(3.99,4.49)	H=2.386	0.303
Cl^-[mmol/L,M(P25,P75)]	105.09(101.16,108.98)	106.33(103.76,108.50)	106.80(104.23,109.38)	H=4.445	0.108
Ca⁺[mmol/L,M(P25,P75)]	2.37(2.28,2.51)	2.32(2.22,2.44)	2.37(2.30,2.43)	H=9.733	0.008
Alanine aminotransferase [U/L,M(P25,P75)]	20.05(12.08,34.70)	16.85(10.80,26.00)	17.20(11.35,30.88)	H=3.067	0.216
Aspartate aminotransferase [U/L,M(P25,P75)]	16.1(12.25,21.73)	14.00(11.30,19.35)	15.5(12.55,21.35)	H=6.759	0.034
Tacrolimus blood concentration [ng/ml,M(P25,P75)]	5.02(3.80,6.83)	4.70(3.30,6.73)	4.62(3.02,6.15)	H=1.412	0.494
Length of stay[n(%)] ≤24 days ＞24 days	33(47.10%) 37(52.90%)	191(52.50%) 173(47.50%)	22(48.90%) 23(51.10%)	=0.789	0.674
Age of donor[years,M(P₂₅,P₇₅)]	42(26,53)	46(32,53)	28(22,46)	H=17.362	＜0.001
Gender of donor[n(%)] Male Female	60(85.70%) 10(14.30%)	273(75.00%) 91(25.00%)	40(88.90%) 5(11.10%)	=7.410	0.025

Immunosuppressive drug regimen: Regimen-A Tacrolimus + mycophenolate mofetil + hormone, regimen-B Tacrolimus + sirolimus + mycophenolate mofetil + hormone, regimen-C Mycophenolate mofetil + cyclosporine + hormone, regimen-D Tacrolimus + cyclosporine + hormone, regimen-E Tacrolimus + cyclosporine + sirolimus + hormone, regimen-F Tacrolimus + mycophenolate mofetil + cyclosporine + hormone, regimen-G Tacrolimus + mizoribine + hormone.

ATG, Anti-human thymoglobulin. R-ATG, Rabbit anti-human thymocyte immunoglobulin

Table 2. Causes of Unplanned Readmissions

Reasons	Unplanned Readmissions(n=183)
Reasons	Donor after cardiac death(n=24)	Donation after brain death(n=136)	Donation after brain plus cardiac death(n=23)
Urinary tract infections[n]	1	21	1
Pulmonary infection[n]	11	43	11
Mixed infection[n]	0	2	0
Other Infections[n]	1	8	0
Reaction to rejection[n]	0	19	3
Proteinuria[n]	1	4	0
Adverse drug reactions[n]	1	14	3
Elevated creatinine[n]	1	5	0
The transplanted kidney was lost[n]	0	4	0
Ureteral stricture in the transplanted kidney[n]	1	2	2
Graft renal artery stenosis[n]	0	2	0
Others[n]	3	9	3

Table 3. Results of Univariate Analysis

Variables of interest	Unplanned readmission group	non-readmission group	Value of statistics	P-value
Serum creatinine[umol/L,M(P25,P75)]	145.00(111.00,197.00)	119.05(95.58,170.50)	Z=-4.043	＜0.001
Cystatin C[mg/L,M(P25,P75)]	2.31(1.81,3.23)	2.04(1.63,2.70)	Z=-3.759	＜0.001
Total protein[G/L,M(P25,P75)]	64.80(61.15,69.30)	66.65(62.45,70.70)	Z=-2.443	0.015
Albumin[G/L,M(P25,P75)]	42.80(40.10,46.00)	44.40(41.45,46.75)	Z=-3.102	0.002
Occupations[n(%)] Yes No	142(72.10%) 51(27.90%)	164(55.40%) 132(44.60%)	=13.400	＜0.001
Comorbidities[n(%)] ＜3 ≥3	162(77.60%) 41(22.40%)	255(86.10%) 41(13.90%)	=5.831	0.016
Rejection reaction[n(%)] Yes No	20(10.90%) 163(89.10%)	12(4.10%) 284(95.90%)	=8.573	0.003
Blood Transfusion during Hospitalization[n(%)] Yes No	23(12.60%) 160(86.40%)	17(5.70%) 279(94.30%)	=6.883	0.009
Fall risk [n(%)] Yes NO	166(90.70%) 17(9.30%)	286(96.60%) 10(3.40%)	=7.429	0.006
Length of stay[n(%)] ≤24 days ＞24 days	76(41.50%) 107(58.50%)	170(57.40%) 126(42.60%)	=11.448	0.001
Variables of interest	Unplanned readmission group	non-readmission group	Value of statistics	P-value
Drinking history[n(%)] Yes No	32(17.50%) 151(82.50%)	32(10.80%) 264(89.20%)	=4.353	0.037

Table 4. Results of Conditional backward stepwise regression Analysis

Variables	B	SE	Wald	P	OR[95%CI]
Serum creatinine	0.004	0.001	13.447	＜0.001	1.004（1.002-1.007）
Albumin	-0.064	0.024	7.088	0.008	0.938（0.895-0.983）
Fall risk	-0.928	0.444	4.358	0.037	0.395（0.165-0.945）
Medical Insurance	0.851	0.308	7.631	0.006	2.343（1.281-4.287）
Length of stay	0.585	0.222	6.929	0.008	1.796（1.161-2.776）
Occupations	1.084	0.260	17.330	＜0.001	2.957（1.775-4.926）
Smoking history	0.606	0.266	5.182	0.023	1.832（1.832-3.087）
Comorbidities	0.623	0.275	5.117	0.024	1.865（1.087-3.200）

Table 5. Variable Assignments

Variables	Description of assignment
Serum creatinine	The original value
Cystatin C	The original value
Total protein	The original value
Albumin	The original value
Fall risk	No=0，Yes=1
Rejection reaction	No=0，Yes=1
Blood Transfusion during Hospitalization	No=0，Yes=1
Medical Insurance	Medical insurance for urban employees=0 Medical insurance for urban residents=1 New rural cooperative medical insurance=2 Out of pocket=3 Other=4
Length of stay	≤24 days=0，＞24 days=1
Occupations	No=0，Yes=1
Drinking history	No=0，Yes=1
Smoking history	No=0，Yes=1
Comorbidities	＜3=0，≥3=1

Table 6. Results of Binary logistic Regression Analysis

Variables	B	SE	Wald	P	OR[95%CI]
Rejection reaction	0.843	0.425	3.928	0.047	2.323（1.009-5.345）
comorbidities greater than or equal to three	0.613	0.266	5.298	1.846	1.887（1.095-3.111）
Fall risk	-0.949	0.445	4.536	0.033	0.387（0.162-0.927）
On-the-job	1.082	0.255	17.977	＜0.001	2.949（1.789-4.862）

No competing interests reported.

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Analyzing the influencing factors of unplanned readmission within one year after cadaveric renal transplantation

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